Center for Gun Violence Prevention and Policy, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA. Electronic address: crifasi@jhu.edu.
Center for Gun Violence Prevention and Policy, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Center for Gun Violence Prevention and Policy, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Center for Gun Violence Prevention and Policy, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Cornell Jeb E. Brooks School of Public Policy, USA.
Center for Gun Violence Prevention and Policy, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the ef...
The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal f...
The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score signif...
CAD software use improved doctors' performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather tha...
Radiographic X-ray imaging is a common clinical examination. Current objective methods for quantifying image quality for radiographs struggle to capture the combined impact of factors throughout the i...
We proposed the image feature index (IFI) to comprehensively quantify radiographic X-ray image quality. We also aimed to study the correlation between IFI and observer-perceived image quality for ches...
The IFI algorithm was developed, which measured the amount of information, textural features, and noise in the image. A total of 70 chest phantom radiographs were generated under 60-120 kV and 0.2-80 ...
The curves of IFI versus DAP and IFI versus EI both demonstrated a similar three-stage form where IFI is above zero: in the first stage, IFI increases rapidly with increased DAP or EI, whereas in the ...
IFI is a feasible and efficient descriptor for image quality for chest radiographs. Future studies with larger sample sizes and sample types are needed to confirm the feasibility of IFI for other exam...
This study was aimed to analyze the application value of the filtered back-projection (FBP) reconstruction algorithm of computed tomography (CT) images in laparoscopic-assisted distal gastrectomy. In ...
Computer-assisted diagnosis (CAD) algorithms have shown its usefulness for the identification of pulmonary nodules in chest x-rays, but its capability to diagnose lung cancer (LC) is unknown. A CAD al...
To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to asses...
Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength l...
DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR...
DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD....
Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern....
To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis....
All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol usin...
Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image qual...
In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quali...
Radiographers extend their roles through formal and on-the-job training to keep up with clinical practice changes. One area of role extension that is now incorporated into undergraduate programmes is ...
A qualitative phenomenological research design was employed to investigate the experiences of ten radiography graduates who were purposively selected from one higher education institution. Individual ...
From the ten interviews conducted, teaching approach, clinical education, and assessment strategy emerged as areas of experience within the teaching and learning theme, while practitioner role modelli...
The participants' experiences reflected a misalignment in the educational process due to inadequacies in the teaching approach, clinical education, and assessment strategies. Participants encountered ...
While these findings are specific to the experiences of the participants, conducting similar research in comparable contexts and implementing competency-based image interpretation assessments could he...
Radiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of un...
A Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only....
86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholder...
Responses indicate that AI will have a strong impact on reporting radiographers' decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining thi...
This survey clarifies UK reporting radiographers' perceptions of AI, used for image interpretation, highlighting key issues with AI integration....
Zambia is experiencing a critical shortage of radiologists responsible for interpreting X-ray images. Nine radiologists serve the entire population of over 18 million people. Consequently, referring p...
A cohort of 27 radiographers employed at eight public hospitals in the Copperbelt Province of Zambia undertook a training intervention using face-to-face training and image discussions on the social m...
The cohort of radiographers (n = 27) showed an improvement in their interpretation skills for trauma CXR images. The interpretation median scores ranged from approximately 82% to 93% in the pre-test a...
This type of novel training intervention is urgently required in the Copperbelt Province of Zambia. The results show that the training process was implemented successfully to improve radiographers' im...
Promoting radiographers' involvement in image interpretation will likely improve imaging services in Zambia, considering the critical shortage of radiologists....
This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. Th...
